from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

reporting = Reporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.013	0.0	6.365	0.0	-1	100	NaN	NaN	0.063	0.0	0.199	0.0	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.012	0.0	6.596	0.0	-1	1	NaN	NaN	0.048	0.0	0.251	0.0	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.012	0.0	6.706	0.0	1	100	NaN	NaN	0.049	0.0	0.244	0.0	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.012	0.0	6.517	0.0	1	1	NaN	NaN	0.049	0.0	0.253	0.0	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.012	0.0	6.721	0.0	-1	5	NaN	NaN	0.049	0.0	0.242	0.0	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	NaN	0.012	0.0	6.726	0.0	1	5	NaN	NaN	0.048	0.0	0.247	0.0	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.005	0.0	0.317	0.0	-1	100	NaN	NaN	0.010	0.0	0.505	0.0	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.006	0.0	0.260	0.0	-1	1	NaN	NaN	0.010	0.0	0.607	0.0	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.005	0.0	0.300	0.0	1	100	NaN	NaN	0.010	0.0	0.525	0.0	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.005	0.0	0.302	0.0	1	1	NaN	NaN	0.010	0.0	0.513	0.0	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.005	0.0	0.300	0.0	-1	5	NaN	NaN	0.010	0.0	0.523	0.0	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	NaN	0.005	0.0	0.301	0.0	1	5	NaN	NaN	0.010	0.0	0.529	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	2.778	0.052	0.000	0.003	-1	100	0.853	0.846	0.243	0.003	11.452	0.272	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.026	0.003	0.000	0.026	-1	100	1.000	1.000	0.008	0.000	3.088	0.378	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	1.915	0.029	0.000	0.002	-1	1	0.669	0.634	0.196	0.002	9.785	0.184	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.024	0.003	0.000	0.024	-1	1	1.000	1.000	0.008	0.000	2.931	0.336	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	2.053	0.021	0.000	0.002	1	100	0.853	0.634	0.195	0.005	10.521	0.268	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.021	0.000	0.000	0.021	1	100	1.000	1.000	0.008	0.000	2.551	0.124	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	1.222	0.006	0.001	0.001	1	1	0.669	0.725	0.195	0.004	6.257	0.123	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.021	0.001	0.000	0.021	1	1	1.000	1.000	0.009	0.001	2.402	0.156	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	2.815	0.032	0.000	0.003	-1	5	0.755	0.846	0.238	0.005	11.806	0.293	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.025	0.003	0.000	0.025	-1	5	1.000	1.000	0.008	0.000	3.012	0.360	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	NaN	2.057	0.010	0.000	0.002	1	5	0.755	0.725	0.198	0.002	10.365	0.109	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	NaN	0.020	0.000	0.000	0.020	1	5	1.000	1.000	0.008	0.000	2.406	0.087	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	2.630	0.030	0.000	0.003	-1	100	0.867	0.872	0.072	0.001	36.691	0.637	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.011	0.002	0.000	0.011	-1	100	1.000	1.000	0.001	0.000	11.580	2.470	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	1.765	0.029	0.000	0.002	-1	1	0.835	0.825	0.031	0.001	56.898	1.344	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.009	0.002	0.000	0.009	-1	1	1.000	0.000	0.001	0.000	10.449	2.810	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	2.176	0.133	0.000	0.002	1	100	0.867	0.825	0.031	0.001	70.246	4.456	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.006	0.001	0.000	0.006	1	100	1.000	0.000	0.001	0.000	7.881	2.370	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	1.234	0.030	0.000	0.001	1	1	0.835	0.863	0.033	0.000	37.244	0.932	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.003	0.002	0.000	0.003	1	1	1.000	1.000	0.001	0.000	3.723	2.113	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	2.861	0.145	0.000	0.003	-1	5	0.862	0.872	0.074	0.001	38.901	2.075	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.010	0.002	0.000	0.010	-1	5	1.000	1.000	0.001	0.000	9.851	2.549	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	NaN	1.943	0.017	0.000	0.002	1	5	0.862	0.863	0.032	0.000	60.416	0.694	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	NaN	0.003	0.000	0.000	0.003	1	5	1.000	1.000	0.001	0.000	3.894	0.778	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.669	0.0	0.717	0.0	random	NaN	30	NaN	0.431	0.0	1.553	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.634	0.0	0.758	0.0	k-means++	NaN	30	NaN	0.507	0.0	1.250	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.185	0.0	3.340	0.0	random	NaN	30	NaN	2.831	0.0	2.538	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.610	0.0	3.631	0.0	k-means++	NaN	30	NaN	2.994	0.0	2.208	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.297	0.000	random	0.001	30	0.001	0.0	0.0	9.895	6.143	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.0	0.0	11.687	7.930	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.297	0.000	k-means++	0.001	30	0.001	0.0	0.0	10.087	6.708	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	12.083	7.556	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.977	0.000	random	0.002	30	0.002	0.0	0.0	6.311	2.426	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.011	0.002	random	1.000	30	1.000	0.0	0.0	14.662	10.850	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.917	0.000	k-means++	0.001	30	0.002	0.0	0.0	6.558	2.578	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.016	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.156	4.606	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	20	0.086	0.0	0.037	0.0	random	NaN	20	NaN	0.032	0.0	2.688	0.0	See	See
3	KMeans_short	fit	10000	10000	2	20	0.249	0.0	0.013	0.0	k-means++	NaN	20	NaN	0.107	0.0	2.333	0.0	See	See
6	KMeans_short	fit	10000	10000	100	20	0.234	0.0	0.685	0.0	random	NaN	20	NaN	0.125	0.0	1.869	0.0	See	See
9	KMeans_short	fit	10000	10000	100	20	0.677	0.0	0.236	0.0	k-means++	NaN	20	NaN	0.349	0.0	1.944	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.156	0.000	random	0.003	20	-0.000	0.001	0.0	3.617	0.891	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	10.416	5.887	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.150	0.000	k-means++	0.005	20	0.001	0.001	0.0	3.753	0.757	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	11.092	6.901	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.467	0.000	random	0.387	20	0.305	0.001	0.0	2.436	0.527	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	random	1.000	20	1.000	0.000	0.0	9.341	5.071	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.375	0.000	k-means++	0.283	20	0.317	0.001	0.0	2.586	0.432	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.027	4.807	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	12.044	0.0	[-0.09796309]	0.000	NaN	NaN	NaN	NaN	NaN	1.977	0.0	6.091	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.877	0.0	[-2.43572838]	0.001	NaN	NaN	NaN	NaN	NaN	0.749	0.0	1.170	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[51.38246118]	0.0	NaN	NaN	NaN	NaN	0.519	0.000	0.0	0.835	0.378	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.2220298]	0.0	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.394	0.350	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[125.0769281]	0.0	NaN	NaN	NaN	NaN	0.290	0.003	0.0	0.528	0.098	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[24.19815601]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.118	0.090	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.184	0.0	0.434	0.0	NaN	NaN	NaN	0.192	0.0	0.960	0.0	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.437	0.0	0.557	0.0	NaN	NaN	NaN	0.254	0.0	5.657	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	NaN	0.01	0.0	7.959	0.0	NaN	NaN	0.116	0.017	0.0	0.593	0.020	See	See
2	Ridge	predict	1000	1	10000	NaN	0.00	0.0	1.051	0.0	NaN	NaN	NaN	0.000	0.0	0.594	0.562	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.00	0.0	5.426	0.0	NaN	NaN	1.000	0.000	0.0	0.577	0.376	See	See
5	Ridge	predict	1000000	1	100	NaN	0.00	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.653	0.643	See	See